Exam Professional Cloud Architect topic 8 question 11 discussion

B is correct. only 200k vehicle's are connected so need to run updates locally

B)=> unconnected vehicles in the field.

B. Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically. Here's why: Capturing all operating data allows you to comprehensively understand vehicle performance under various conditions. Training machine learning models enables you to identify the ideal operational parameters for each vehicle based on environmental and operational conditions. Running these models locally on the vehicles allows real-time adjustments to be made, even for unconnected vehicles, enhancing operational efficiency without requiring constant communication with the cloud. This is especially important for vehicles that may not have a constant cellular connection.

i think the issue here is that whether or not you want the model learn itself by continue provide the environment condition data . if you build and train the model in old data and deploy it local , will expect no continue learning by fitting new data.

I choose answer D. For answer B, running ML models locally on each vehicle is resource-intensive and might not be feasible for all vehicle types. It also makes updating and managing the models more difficult.

Option B seems to be the most comprehensive approach. By capturing all operating data and training machine learning models to identify ideal operations, you can adaptively adjust operational parameters for each vehicle, thereby increasing overall efficiency. Hosting these models locally ensures real-time adjustments can be made autonomously without relying on external services or cloud infrastructure. The issue with option D is that it relies on hosting machine learning models in the Google Cloud Machine Learning Platform. While this could still potentially improve efficiency, it introduces additional latency and dependency on external cloud services, which might not be ideal for real-time operational adjustments required in the field. Additionally, hosting the models in the cloud could incur ongoing costs and potential connectivity issues, which might not be suitable for all operational environments.

It is D

I don't understand why everyone votes for B. In the replies I see, it seems to me that people are expecting the ML model to run on the vehicles and make changes offline. For this to be true, the vehicles would need a powerful computer, at least if this model is anywhere close to the AI models around... So, in this case, the ML model would have to be uploaded to all 20 million vehicles for them to change this parameters offline? That seems kind of crazy, but maybe I'm lacking some kind of info? Maybe the ML models are very portable and don't require much processing power?

Also for me it's B. Maybe it might not seem so straightforward when reading the answer, but personally I understood it as 'running the result of ML training on devices'. Understanding it this way it certainly can only be B as from the scenario description we have most of the machines not connected to a cellular network and therefore it is impossible to pass data to them.

B is correct. only 200k vehicle's are connected so need to run updates locally, means ML Edge

Answer is B

ok for B

B is correct. only 200k vehicle's are connected so need to run updates locally

B is right , Agreed with detailed discussions !

B. Train model in the cloud and deploy model to the edge for local prediction. This is typical in IoT.

vote B

from exam point of view, I doubt these questions would be part of new exam. Terram earth case study has changed.